Professor Thomas Lumley

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Chair in Biostatistics


I attended Monash University (B.Sc.(Hons) in Pure Mathematics), the University of Oxford (M.Sc. in Applied Statistics) and the University of Washington, Seattle (PhD in Biostatistics). I spent twelve years on the faculty of the Department of Biostatistics at the University of Washington, and then moved to Auckland in 2010. I am still an Affiliate Professor at the University of Washington.

Research | Current

My research interests include

  • Semiparametric models
  • Survey sampling
  • Statistical computing
  • Foundations of statistics
  • and whatever methodological problems his medical collaborators come up with -- currently, multiple imputation on big datasets


  • The survey package for R is a fairly comprehensive system for analysis of data from complex probability samples.
  • I have written a book on survey analysis, published by Wiley.

Teaching | Current

  • STATS 369 S2 2018
  • STATS 763 S1 2019


Postgraduate supervision

Potential student projects

  • Likelihood of the empirical distribution function as an approach to Bayesian analysis of survey data.
  • Mixed models under complex sampling -- and maybe network models, too
  • Survey software: design and implementation of various things for the survey package in R or for database backends. Graphics, probability distributions, regression models, multivariate methods...
  • Software for teaching:design and implementation of interface to R for teaching introductory biostatistics
  • How many categories? Data on, say, hospital visits, can be categorized coarsely (eg lung problem) or much more finely (eg infection by pencillin-resistant Strep. pneumoniae). When the categories are too fine the sample size in each one is too small to see patterns; when they are too coarse, the patterns are masked by events that don't really belong. The idea is to build a tree structure that uses all levels of categorization simultaneously in a Bayesian model.


Fellow of the Royal Society of New Zealand
Fellow of the American Statistical Association

Selected publications and creative works (Research Outputs)

  • Lumley, T. (2019). Fast Generalized Linear Models by Database Sampling and One-Step Polishing. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS10.1080/10618600.2019.1610312
  • Oh, E. J., Shepherd, B. E., Lumley, T., & Shaw, P. A. (2018). Considerations for analysis of time-to-event outcomes measured with error: Bias and correction with SIMEX. Statistics in medicine, 37 (8), 1276-1289. 10.1002/sim.7554
  • Rice, K., Higgins, J. P. T., & Lumley, T. (2018). A re-evaluation of fixed effect(s) meta-analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society, 181 (1), 205-227. 10.1111/rssa.12275
  • Lumley, T., & Scott, A. (2017). Fitting Regression Models to Survey Data. Statistical Science, 32 (2), 265-278. 10.1214/16-STS605
  • Rivera, C. L., & Lumley, T. (2016). Using the entire history in the analysis of nested case cohort samples. Statistics in medicine, 35 (18), 3213-3228. 10.1002/sim.6917
    Other University of Auckland co-authors: Claudia Rivera Rodriguez
  • Rivera, C., & Lumley, T. (2016). Using the whole cohort in the analysis of countermatched samples. Biometrics, 72 (2), 382-391. 10.1111/biom.12419
    Other University of Auckland co-authors: Claudia Rivera Rodriguez
  • Conomos, M. P., Laurie, C. A., Stilp, A. M., Gogarten, S. M., McHugh, C. P., Nelson, S. C., ... Graff, M. (2016). Genetic diversity and association studies in US Hispanic/Latino populations: Applications in the Hispanic Community Health Study/Study of Latinos. American Journal of Human Genetics, 98 (1), 165-184. 10.1016/j.ajhg.2015.12.001
  • Lumley, T., & Scott, A. (2015). AIC and BIC for modeling with complex survey data. Journal of Survey Statistics and Methodology, 3 (1), 1-18. 10.1093/jssam/smu021

Contact details

Primary office location

Level 3, Room 325
New Zealand